Machine learning (ML) can be applied to various computer vision applications, including object detection and image classification (or “image recognition”). General object detection can be used to locate an object (e.g., a car or a bird) within an image, whereas image classification may involve a relatively fine-grained classification of the image (e.g., a 1969 Beetle, or an American Goldfinch). Convolutional Neural Networks (CNNs) are commonly used for both image classification and object detection. A CNN is a class of deep, feed-forward artificial neural networks that has successfully been applied to analyzing visual imagery. Generalized object detection may require models that are relatively large and computationally expensive, presenting a challenge for resource-constrained devices such as some smartphones and tablet computers. In contrast, image recognition may use relatively small models and require relatively little processing.